Data Mart is a pattern of structures/access specific to the data warehouse environment, used to retrieve data facing clients. Data marts are part of the data warehouse and are typically oriented towards a particular business or team line. While the data warehouse has a wide depth of enterprise, the information in the data mart is related to one department. In some applications, each department or business unit is considered the owner of its data mart including all hardware , software and data . This allows each department to isolate the use, manipulation, and development of their data. In other applications where appropriate dimensions are used, the ownership of this business unit will not apply to shared dimensions such as customers, products, etc.
Organizations build data warehouses and data marts because the information in the database is not regulated in a way that makes it accessible, requires too complicated queries or consumes a lot of resources.
While the transactional database is designed to be updated, the data warehouse or mart can only be read. The data warehouse is designed to access a large group of related recordings. Mart data increases the response time of end users by allowing users to have access to the specific types of data they see most often by providing data in a way that supports the collective view of a group of users.
Data mart is basically a more solid and more focused version of the data warehouse that reflects the regulatory and process specifications of each business unit within an organization. Each data mart is dedicated to a particular function or business area. This data subset can reach many or all areas of the functional subject of the company. It is common for some data marts to be used to serve the needs of each business unit (various data marts can be used to get specific information for different departments of a company, such as accounting, marketing, sales, etc.).
The term related spreadmart is an insulting label describing a situation that occurs when one or more business analysts develop a related spreadsheet system to conduct business analysis, then grow it to a size and level of complexity that makes it virtually impossible to maintain.
Video Data mart
Data mart vs data warehouse
Data warehouse:
- Saving multiple subject fields
- Store very detailed information
- Works to integrate all data sources
- There is no need to use dimension models but provide dimension model feeds.
Data mart:
- Often have only one subject area - for example, Finance, or Sales
- Can store more summary data (though may contain full details)
- Concentrate on integrating information from a particular subject area or set of source systems
- Built focuses on dimensional models using star schemes.
Maps Data mart
Design scheme
- Star scheme - a pretty popular design choice; allows relational databases to mimic the analytical functions of a multidimensional database
- Snowflake Scheme
Reason for creating data mart
- Easy access to frequently needed data
- Create a collective view by a group of users
- Increase end-user response time
- Ease of creation
- Cost is lower than applying a full data warehouse
- Potential users are more obvious than in the full data warehouse
- contains only important business data and is less cluttered.
Mart data dependent
According to the Inmon School of data warehousing, mart data dependent is a logical subset (view) or a physical subset (extract) from a larger data warehouse, isolated for one of the following reasons:
- Refresh the need for a custom data model or schema: for example, to restructure OLAP
- Performance: to issue data marts to separate computers for greater efficiency or to eliminate the need to manage the workload on a centralized data warehouse.
- Security: to selectively divide the subset of official data
- Utility: to bypass the data governance and authorizations needed to merge new apps in the Corporate Data Warehouse
- Proving Ground: to demonstrate the potential and potential investment ROI (return on investment) of the app before migrating it to the Corporate Data Warehouse
- Politics: coping strategies for IT (Information Technology) in situations where user groups have more influence than funding or are not good citizens in a centralized data warehouse.
- Politics: coping strategies for data consumers in situations where data warehouse teams can not create usable data warehouses.
According to Inmon schools of data warehousing, the inherent tradeoff with data mart includes unlimited scalability, data duplication, data inconsistency with other information silos, and inability to utilize corporate data sources.
The alternative school of data warehousing belongs to Ralph Kimball. In his view, the data warehouse is nothing more than the unification of all data marts. This view helps reduce costs and provides rapid development, but can create inconsistent data warehouses, especially in large organizations. Therefore, Kimball's approach is more suitable for small and medium-sized companies.
See also
- Data warehouse Company architecture
- OLAP cube
References
Bibliography
Inca, William (2000-07-18). "Data Mart Is Not the Same as Data Warehouse". DMReview.com. Archived from the original on 2011-04-20.
External links
Source of the article : Wikipedia
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